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Record W4247276606 · doi:10.32920/ryerson.14648655

3-D modeling and simulation of crystal growth of GE₀.₉₈ Si₀.₀₂ under the influence of various gravity levels, G-jitter and rotating magnetic field using traveling solvent method

2021· preprint· en· W4247276606 on OpenAlexaff
Mehdi Mohammadi Shemirani

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRotating magnetic fieldMagnetic fieldMaterials scienceMechanicsBuoyancyMass transferConvectionGravitational accelerationHomogeneity (statistics)Intensity (physics)Computational physicsPhysicsOpticsGravitational fieldClassical mechanics

Abstract

fetched live from OpenAlex

A three-dimensional numerical simulation was conducted to study the effect of a rotating magnetic (RMF) field on the fluid flow, heat transfer and mass transfer in the presence of various gravity levels by utilizing the traveling solvent method (TSM). The presence of the RMF suppressed the buoyancy convection in the GE₀.₉₈ Si₀.₀₂ solution zone in order to get homogeneity with a flat growth interface. It was found that the intensity of the flow at the centre of the crucible decreased at a faster rate compared to the flow near the walls when increasing magnetic field intensity is combined with a certain rotational speed. This behavior created a stable and uniform silicon distribution in the horizontal plane near the growth interface in the terrestrial condition. Different magnetic field intensities for different rotational speeds were examined in both terrestrial and micro-gravity conditions. The effects of residual acceleration, known as G-jitter, on board the International Space Station and European Space Orbiter were also investigated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.317
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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